Research Article
Recommendation Based Heterogeneous Information Network and Neural Network Model
@INPROCEEDINGS{10.1007/978-3-030-69072-4_48, author={Cong Zhao and Yan Wen and Ming Chen and Geng Chen}, title={Recommendation Based Heterogeneous Information Network and Neural Network Model}, proceedings={Wireless and Satellite Systems. 11th EAI International Conference, WiSATS 2020, Nanjing, China, September 17-18, 2020, Proceedings, Part II}, proceedings_a={WISATS PART 2}, year={2021}, month={2}, keywords={Heterogeneous information network representation learning Neural network Recommendation system}, doi={10.1007/978-3-030-69072-4_48} }- Cong Zhao
Yan Wen
Ming Chen
Geng Chen
Year: 2021
Recommendation Based Heterogeneous Information Network and Neural Network Model
WISATS PART 2
Springer
DOI: 10.1007/978-3-030-69072-4_48
Abstract
With the advent of the Internet era, the recommendation system has developed rapidly. Heterogeneous information networks representation learning is widely used in recommendation systems due to its advantages in complex information modeling. Although the performance of the recommendation system has been improved, there are still two shortcomings: 1. There is a lot of noise data in the instances of the meta-path generated by random walks of meta-paths, which will reduce the performance of the recommendation system. 2. Traditional recommendation algorithms fail to make full use of the relevant meta-path information in heterogeneous information networks, which makes the recommendation results lack of interpretability. To solve these problems, we propose a recommendation system based on heterogeneous network representation learning and neural network model. Firstly, use the matrix factorization and the similarity calculation to select the meta-path instances with good quality as the pre-training vectors of the recommendation system. Then, we combine LSTM with Attention mechanism to learn the user, item and meta-path embeddings, and use MLP to make prediction after fusion to jointly improve the recommendation effect. We conducted experiments on Movielens datasets to evaluate the performance of our proposed recommendation.
